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Firstly, we perform community clustering on different social networks, calculate the similarity between different network communities, and screen out community pairs with greater similarity. Secondly, two\u2010way marriage matching is carried out for users between pairs of communities with high similarity. Then, the dynamic community clustering was performed by resetting the different community clustering numbers. Finally, the iteration is repeated until no new matching user pairs are generated, or the set number of iterations is reached. Experiments conducted on real\u2010world social networks Twitter\u2010Foursquare datasets demonstrate that compared with the global user matching method and hidden label node method, the average accuracy of the proposed UIUTDC algorithm is improved by 33% and 26.8%, respectively. In the case of only user topology information, the proposed UIUTDC algorithm effectively improves the accuracy of identity recognition in practical applications.<\/jats:p>","DOI":"10.1155\/2021\/5567351","type":"journal-article","created":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T20:35:07Z","timestamp":1618691707000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Two\u2010Stage User Identification Based on User Topology Dynamic Community Clustering"],"prefix":"10.1155","volume":"2021","author":[{"given":"Jiajing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5547-6079","authenticated-orcid":false,"given":"Zhenhua","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juxiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,17]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"45","article-title":"Cross-social network user identity matching based on fusion representation learning","volume":"44","author":"Yang Y.","year":"2018","journal-title":"Computer Engineering"},{"doi-asserted-by":"publisher","key":"e_1_2_9_2_2","DOI":"10.1049\/iet-cta.2016.1237"},{"doi-asserted-by":"crossref","unstructured":"ZafaraniR.andLiuH. 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